Tag Archives: long-term plasticity (LTP)

Changing synaptic connectivity with a memristor

The French have announced some research into memristive devices that mimic both short-term and long-term neural plasticity according to a Dec. 6, 2016 news item on Nanowerk,

Leti researchers have demonstrated that memristive devices are excellent candidates to emulate synaptic plasticity, the capability of synapses to enhance or diminish their connectivity between neurons, which is widely believed to be the cellular basis for learning and memory.

The breakthrough was presented today [Dec. 6, 2016] at IEDM [International Electron Devices Meeting] 2016 in San Francisco in the paper, “Experimental Demonstration of Short and Long Term Synaptic Plasticity Using OxRAM Multi k-bit Arrays for Reliable Detection in Highly Noisy Input Data”.

Neural systems such as the human brain exhibit various types and time periods of plasticity, e.g. synaptic modifications can last anywhere from seconds to days or months. However, prior research in utilizing synaptic plasticity using memristive devices relied primarily on simplified rules for plasticity and learning.

The project team, which includes researchers from Leti’s sister institute at CEA Tech, List, along with INSERM and Clinatec, proposed an architecture that implements both short- and long-term plasticity (STP and LTP) using RRAM devices.

A Dec. 6, 2016 Laboratoire d’électronique des technologies de l’information (LETI) press release, which originated the news item, elaborates,

“While implementing a learning rule for permanent modifications – LTP, based on spike-timing-dependent plasticity – we also incorporated the possibility of short-term modifications with STP, based on the Tsodyks/Markram model,” said Elisa Vianello, Leti non-volatile memories and cognitive computing specialist/research engineer. “We showed the benefits of utilizing both kinds of plasticity with visual pattern extraction and decoding of neural signals. LTP allows our artificial neural networks to learn patterns, and STP makes the learning process very robust against environmental noise.”

Resistive random-access memory (RRAM) devices coupled with a spike-coding scheme are key to implementing unsupervised learning with minimal hardware footprint and low power consumption. Embedding neuromorphic learning into low-power devices could enable design of autonomous systems, such as a brain-machine interface that makes decisions based on real-time, on-line processing of in-vivo recorded biological signals. Biological data are intrinsically highly noisy and the proposed combined LTP and STP learning rule is a powerful technique to improve the detection/recognition rate. This approach may enable the design of autonomous implantable devices for rehabilitation purposes

Leti, which has worked on RRAM to develop hardware neuromorphic architectures since 2010, is the coordinator of the H2020 [Horizon 2020] European project NeuRAM3. That project is working on fabricating a chip with architecture that supports state-of-the-art machine-learning algorithms and spike-based learning mechanisms.

That’s it folks.

Artificial synapse rivals biological synapse in energy consumption

How can we make computers be like biological brains which do so much work and use so little power? It’s a question scientists from many countries are trying to answer and it seems South Korean scientists are proposing an answer. From a June 20, 2016 news item on Nanowerk,

News) Creation of an artificial intelligence system that fully emulates the functions of a human brain has long been a dream of scientists. A brain has many superior functions as compared with super computers, even though it has light weight, small volume, and consumes extremely low energy. This is required to construct an artificial neural network, in which a huge amount (1014)) of synapses is needed.

Most recently, great efforts have been made to realize synaptic functions in single electronic devices, such as using resistive random access memory (RRAM), phase change memory (PCM), conductive bridges, and synaptic transistors. Artificial synapses based on highly aligned nanostructures are still desired for the construction of a highly-integrated artificial neural network.

Prof. Tae-Woo Lee, research professor Wentao Xu, and Dr. Sung-Yong Min with the Dept. of Materials Science and Engineering at POSTECH [Pohang University of Science & Technology, South Korea] have succeeded in fabricating an organic nanofiber (ONF) electronic device that emulates not only the important working principles and energy consumption of biological synapses but also the morphology. …

A June 20, 2016 Pohang University of Science & Technology (POSTECH) news release on EurekAlert, which originated the news item, describes the work in more detail,

The morphology of ONFs is very similar to that of nerve fibers, which form crisscrossing grids to enable the high memory density of a human brain. Especially, based on the e-Nanowire printing technique, highly-aligned ONFs can be massively produced with precise control over alignment and dimension. This morphology potentially enables the future construction of high-density memory of a neuromorphic system.

Important working principles of a biological synapse have been emulated, such as paired-pulse facilitation (PPF), short-term plasticity (STP), long-term plasticity (LTP), spike-timing dependent plasticity (STDP), and spike-rate dependent plasticity (SRDP). Most amazingly, energy consumption of the device can be reduced to a femtojoule level per synaptic event, which is a value magnitudes lower than previous reports. It rivals that of a biological synapse. In addition, the organic artificial synapse devices not only provide a new research direction in neuromorphic electronics but even open a new era of organic electronics.

This technology will lead to the leap of brain-inspired electronics in both memory density and energy consumption aspects. The artificial synapse developed by Prof. Lee’s research team will provide important potential applications to neuromorphic computing systems and artificial intelligence systems for autonomous cars (or self-driving cars), analysis of big data, cognitive systems, robot control, medical diagnosis, stock trading analysis, remote sensing, and other smart human-interactive systems and machines in the future.

Here’s a link to and a citation for the paper,

Organic core-sheath nanowire artificial synapses with femtojoule energy consumption by Wentao Xu, Sung-Yong Min, Hyunsang Hwang, and Tae-Woo Lee. Science Advances  17 Jun 2016: Vol. 2, no. 6, e1501326 DOI: 10.1126/sciadv.1501326

This paper is open access.